note = "Proceedings of the 17th Online World Conference on
Soft Computing in Industrial Applications",

keywords = "genetic algorithms, genetic programming",

isbn13 = "978-3-319-00929-2",

URL = "http://dx.doi.org/10.1007/978-3-319-00930-8_4",

DOI = "doi:10.1007/978-3-319-00930-8_4",

language = "English",

abstract = "In this chapter, a new GP-based algorithm is proposed.
The algorithm, named SGP (Statistical GP), exploits
statistical information, i.e. mean, variance and
correlation-based operators, in order to improve the GP
performance. SGP incorporates new genetic operators,
i.e. Correlation Based Mutation, Correlation Based
Crossover, and Variance Based Editing, to drive the
search process towards fitter and shorter solutions.
Furthermore, this work investigates the correlation
between diversity and fitness in SGP, both in terms of
phenotypic and genotypic diversity. First experiments
conducted on four symbolic regression problems
illustrate the goodness of the approach and permits to
verify the different behaviour of SGP in comparison
with standard GP from the point of view of the
diversity and its correlation with the fitness.",